Context. With the introduction of Computer Science (CS) into curricula worldwide, teachers’ adoption of CS pedagogical content is essential to ensure the long-term success of reform initiatives. Continuing Professional Development (CPD) programs play a key role in this process. Unfortunately, adoption is seldom evaluated in CS-CPDs or CPDs in general. The result is a dearth of studies (i) modelling teachers’ adoption of CS pedagogical content or (ii) investigating factors influencing the uptake of this new discipline. Both aspects are crucial to design and characterize successful CPD programs. Objectives . We thus propose the Teachers’ Adoption of CS (TACS) model to investigate factors influencing the adoption of CS pedagogical content by teachers who are following a mandatory CS-CPD program. More specifically, the model proposes that contextual factors (e.g., age, gender, and general teaching experience), prior factors (e.g., experience, and CS perception), and acceptance factors (e.g., interest, and self-efficacy) may impact teachers’ adoption of CS pedagogical content. Methods. The study included 180 grades 5 and 6 teachers (students aged 9–11) that were following a mandatory CS-CPD program. The CS-CPD program involved participation in three-day-long sessions distributed over the 2019–2020 academic year. In between sessions, with the support of instructional coaches in the schools, teachers were encouraged, but not required, to adopt the CS pedagogical content. Therefore, during the CPD, and employing surveys based on the TACS model, we evaluated teachers’ adoption of the proposed content and investigated how the different factors influenced it. Results. At the PD-level, the results indicate that self-efficacy and interest queried during the CS-CPD are indicative of CS pedagogical content adoption. To shed more light on the relationship between these metrics, a more in-depth analysis was conducted with n = 92 teachers whose responses could be matched between sessions. While interest relates to how teachers adopt CS pedagogical content overall, both interest and self-efficacy are necessary to ensure the likelihood of a specific activity being adopted. Finally, individual teacher characteristics appear to impact adoption, with teachers with low experience with Information and Communication Technologies (ICT) requiring onboarding, while middle-aged teachers require convincing to adopt CS pedagogical content. Conclusion. Three takeaways emerge from the study. First, the analyses confirm the foundation of the TACS model. Second, the findings establish the key role that interest plays in said model. Finally, the results support the relationship between the contextual, prior and acceptance factors on the adoption of primary school CS pedagogical content.

Context Introducing Computer Science (CS) into formal education can be challenging, notably when considering the numerous stakeholders involved which include the students, teachers, schools, and policy makers. We believe these perspectives should be considered conjointly, which is possible within Research Practice Partnerships (RPPs) . RPPs look to bridge research-practice gaps and have seen an increase in the field of education and CS-education. Unfortunately, RPPs are considered to be under-researched, in addition to presenting their own challenges. Objectives To the purpose of assessing how RPPs may support the successful introduction of CS into formal education, we investigate three perspectives (students, teachers, and RPP stakeholders) and their interplay within the context of a multi-institution RPP conducting a pilot program to introduce CS to secondary school students. Methods A mixed methods analysis was employed to triangulate data in a concurrent triangulation design. The data included (i) three surveys distributed over the semester to 106 grade 9 students (ages 12-14), (ii) four teacher-journals, (iii) two interviews and four focus groups with the teachers and representatives of the partner institutions. Findings From the students’ perspective , while their self-efficacy increased, their motivation decreased throughout the semester due to a miss-match between their expectations and the course. The findings also indicate that gender biases and heterogeneity are already present in grade 9. From the teachers’ perspective , co-constructing the study plan, having access to regular support and collaborating within a community of practice when starting to teach CS all facilitated the teachers’ experience. Finally, from the RPP’s perspective the collaboration between stakeholders and having researchers evaluate the program were considered to be key elements in the pilot program. However, there appears to be a research-practice gap, in large part due to limited interactions between researchers and curriculum designers, and researchers and the teachers in the field. Conclusions From the students’ perspective it appears relevant to introduce CS (i) prior to secondary school to address motivation and bias-related issues early on, and (ii) to all students to avoid participation being motivation-, stereotype-, or belief-driven, and risk broadening the gap between students, (iii) all the while being attentive to course format and content to ensure that the course meets students’ expectations and fosters autonomous motivation. From the teachers’ perspective , while the support provided met the teachers’ needs, it is essential to find means of scaling such approaches when looking to deploy CS-curricular reforms to entire administrative regions. Finally, from the RPP’s perspective (i) teachers’ should be given a voice in the RPP to better align with the field, and (ii) researchers’ roles should be reconsidered to move beyond being only evaluators, and towards having a more co-constructive role in setting up the curricular reform. Recommendations are provided for researchers and practitioners involved in CS curricular reforms.

Abstract Research has shown that Educational Robotics (ER) enhances student performance, interest, engagement and collaboration. However, until now, the adoption of robotics in formal education has remained relatively scarce. Among other causes, this is due to the difficulty of determining the alignment of educational robotic learning activities with the learning outcomes envisioned by the curriculum, as well as their integration with traditional, non-robotics learning activities that are well established in teachers’ practices. This work investigates the integration of ER into formal mathematics education, through a quasi-experimental study employing the Thymio robot and Scratch programming to teach geometry to two classes of 15-year-old students, for a total of 26 participants. Three research questions were addressed: (1) Should an ER-based theoretical lecture precede, succeed or replace a traditional theoretical lecture? (2) What is the students’ perception of and engagement in the ER-based lecture and exercises? (3) Do the findings differ according to students’ prior appreciation of mathematics? The results suggest that ER activities are as valid as traditional ones in helping students grasp the relevant theoretical concepts. Robotics activities seem particularly beneficial during exercise sessions: students freely chose to do exercises that included the robot, rated them as significantly more interesting and useful than their traditional counterparts, and expressed their interest in introducing ER in other mathematics lectures. Finally, results were generally consistent between the students that like and did not like mathematics, suggesting the use of robotics as a means to broaden the number of students engaged in the discipline.

Past research has shown that the use of tangible programming platforms in computing education can enhance students' interest, engagement, and collaboration within workgroups. However, to this day, the adoption of such interfaces in classrooms has remained relatively scarce. This is possibly due to the expenses and efforts necessary to acquire, set up and maintain such platforms. In this context, the use of paper as a principal means of interaction represents an inexpensive and versatile solution, that additionally harnesses the prevalence of paper in classrooms. This work, therefore, introduces PaPL, an easily reproducible platform for paper-based programming languages. The platform was evaluated in two exploratory user studies. The first study aimed at investigating the interaction of over 100 senior year high school students with the platform under varying conditions of group size and usage constraints. In the second study, the platform was tested with 32 sixth-graders and 2 teachers to evaluate its usage in an authentic context. The results indicate that group size may affect active discussion and error count, while usage constraints may affect active discussion of students interacting with the platform. Moreover, the classroom study shows promising results with regard to the use of PaPL in formal education.

In recent years, robots have been surfing on a trendy wave as standard devices for teaching programming. The tangibility of robotics platforms allows for collaborative and interactive learning. Moreover, with these robot platforms, we also observe the occurrence of a shift of visual attention from the screen (on which the programming is done) to the physical environments (i.e. the robot). In this paper, we describe an experiment aiming at studying the effect of using augmented reality (AR) representations of sensor data in a robotic learning activity. We designed an AR system able to display in real-time the data of the Infra-Red sensors of the Thymio robot. In order to evaluate the impact of AR on the learner's understanding on how these sensors worked, we designed a pedagogical lesson that can run with or without the AR rendering. Two different age groups of students participated in this between-subject experiment, counting a total of 74 children. The tests were the same for the experimental (AR) and control group (no AR). The exercises differed only through the use of AR. Our results show that AR was worth being used for younger groups dealing with difficult concepts. We discuss our findings and propose future works to establish guidelines for designing AR robotic learning sessions.

Rehabilitation aims to ameliorate deficits in motor control via intensive practice with the affected limb. Current strategies, such as one-on-one therapy done in rehabilitation centers, have limitations such as treatment frequency and intensity, cost and requirement of mobility. Thus, a promising strategy is home-based therapy that includes task specific exercises. However, traditional rehabilitation tasks may frustrate the patient due to their repetitive nature and may result in lack of motivation and poor rehabilitation. In this article, we propose the design and verification of an effective upper extremity rehabilitation game with a tangible robotic platform named Cellulo as a novel solution to these issues. We first describe the process of determining the design rationales to tune speed, accuracy and challenge. Then we detail our iterative participatory design process and test sessions conducted with the help of stroke, brachial plexus and cerebral palsy patients (18 in total) and 7 therapists in 4 different therapy centers. We present the initial quantitative results, which support several aspects of our design rationales and conclude with our future study plans.

The Cellulo robots are small tangible robots that are designed to represent virtual interactive point-like objects that reside on a plane within carefully designed learning activities. In the context of these activities, our robots not only display autonomous motion and act as tangible interfaces, but are also usable as haptic devices in order to exploit, for instance, kinesthetic learning. In this article, we present the design and analysis of the haptic interaction module of the Cellulo robots. We first detail our hardware and controller design that is low-cost and versatile. Then, we describe the task-based experimental procedure to evaluate the robot's haptic abilities. We show that our robot is usable in most of the tested tasks and extract perceptive and manipulative guidelines for the design of haptic elements to be integrated in future learning activities. We conclude with limitations of the system and future work.

This article presents a learning activity and its user study involving the Cellulo platform, a novel versatile robotic tool designed for education. In order to show the potential of Cellulo in the classroom as part of standard curricular activities, we designed a learning activity called Windfield that aims to teach the atmospheric formation mechanism of wind to early middle school children. The activity involves a didactic sequence, introducing the Cellulo robots as hot air balloons and enabling children to feel the wind force through haptic feedback. We present a user study, designed in the form of a real hour-long lesson, conducted with 24 children in 8 groups who had no prior knowledge in the subject. Collaborative metrics within groups and individual performances about the learning of key concepts were measured with only the hardware and software integrated in the platform in a completely automated manner. The results show that almost all participants showed learning of symmetric aspects of wind formation while about half showed learning of asymmetric vectorial aspects that are more complex.

In this article, we present Cellulo, a novel robotic platform that investigates the intersection of three ideas for robotics in education: designing the robots to be versatile and generic tools; blending robots into the classroom by designing them to be pervasive objects and by creating tight interactions with (already pervasive) paper; and finally considering the practical constraints of real classrooms at every stage of the design. Our platform results from these considerations and builds on a unique combination of technologies: groups of handheld haptic-enabled robots, tablets and activity sheets printed on regular paper. The robots feature holonomic motion, haptic feedback capability and high accuracy localization through a microdot pattern overlaid on top of the activity sheets, while remaining affordable (robots cost about EUR 125 at the prototype stage) and classroom-friendly. We present the platform and report on our first interaction studies, involving about 230 children.

Bio-mimetic robots can interact with groups of animals in bio-hybrid systems to study their behaviour by producing calibrated stimuli and by analysing their responses. Integrating a group of robots into a group of animals to mimic their behaviour is challenging, both in terms of robotic hardware design and robot control. In particular, the robots must be able to react in real-time to the animal changes of behaviour. This implies the need to adequately track and identify animal behaviour. In this paper, we present a novel framework to control several bio-mimetic robots to integrate into groups of fish. Our framework is able to track the position of the fish and robots in real-time. The robots are driven by a bio-mimetic model of fish behaviour from the literature. We show that our multi-robot system can successfully integrate into groups of fish with closed-loop interactions between the robots and the fish.

The strong interest children show for mobile robots makes these devices potentially powerful to teach programming. Moreover, the tangibility of physical objects and the sociability of interacting with them are added benefits. A key skill that novices in programming have to acquire is the ability to mentally trace program execution. However, because of their embodied and real-time nature, robots make the mental tracing of program execution difficult.

Building over algorithms previously developed for digital pens, this article introduces a novel 2D localization technique for mobile robots, based on simple printed patterns. This method combines high absolute accuracy (below 0.3mm), unlimited scalability, low computational requirements (the presented open-source implementation runs at above 45Hz on a low-cost microcontroller) and low cost (below €30 per device at prototype stage). The article first presents the underlying algorithms and localization pipeline. It then describes our reference hardware and software implementations, and finally evaluates the performance of this technique for mobile robots.

This paper overviews our application of state-of-the-art automated planning algorithms to real mobile robots performing an autonomous construction task, a domain in which robots are prone to faults. We describe how embracing these faults leads to better representations and smarter planning, allowing robots with limited precision to avoid catastrophic failures and succeed in intricate constructions.

We present the design approach and evaluation of our prototype called "Ranger". Ranger is a robotic toy box that aims to motivate young children to tidy up their room. We evaluated Ranger in 14 families with 31 children (2-10 years) using the Wizard-of-Oz technique. This case study explores two different robot behaviors (proactive vs. reactive) and their impact on children's interaction with the robot and the tidying behavior. The analysis of the video recorded scenarios shows that the proactive robot tended to encourage more playful and explorative behavior in children, whereas the reactive robot triggered more tidying behavior. Our findings hold implications for the design of interactive robots for children, and may also serve as an example of evaluating an early version of a prototype in a real-world setting.

We present a communication based navigation algorithm for robotic swarms. It lets robots guide each other's navigation by exchanging messages containing navigation information through the wireless network formed among the swarm. We study the use of this algorithm in two different scenarios. In the first scenario, the swarm guides a single robot to a target, while in the second, all robots of the swarm navigate back and forth between two targets. In both cases, the algorithm provides efficient navigation, while being robust to failures of robots in the swarm. Moreover, we show that in the latter case, the system lets the swarm self-organize into a robust dynamic structure. This self-organization further improves navigation efficiency, and is able to find shortest paths in cluttered environments. We test our system both in simulation and on real robots.

We introduce enhanced directional self-assembly (EDSA) - a novel mechanism for morphology growth through the creation of directed connections in a self-assembling multirobot system. In our approach, a robot inviting a physical connection actively recruits the best located neighboring robot and guides the recruit to the location on its chassis where the connection is required. The proposed mechanism relies on local, high-speed communication between connection inviting robots and their recruits. Communication is based on a hybrid technology that combines radio and infrared to provide local relative positioning information when messages are transmitted between adjacent robots. Experiments with real robotic hardware show that EDSA is precise (misalignment of only 1.2° on average), robust (100% success rate for the experiments in this study) and fast (16.1 seconds on average from a distance of 80 cm). We show how the speed and precision of the new approach enable adaptive recruitment and connection in dynamic environments, a high degree of parallelism, and growth of a moving morphology.

Simultaneous localization and mapping (SLAM) is a prominent feature for autonomous robots operating in undefined environments. Applications areas such as consumer robotics appliances would clearly benefit from low-cost and compact SLAM implementations. The SLAM research community has developed several robust algorithms in the course of the last two decades. However, until now most SLAM demonstrators have relied on expensive sensors or large processing power, limiting their realms of application. Several works have explored optimizations into various directions; however none has presented a global optimization from the mechatronic to the algorithmic level. In this article, we present a solution to the SLAM problem based on the co-design of a slim rotating distance scanner, a lightweight SLAM software, and an optimization methodology. The scanner consists of a set of infrared distance sensors mounted on a contactless rotating platform. The SLAM algorithm is an adaptation of FastSLAM 2.0 that runs in real time on a miniature robot. The optimization methodology finds the parameters of the SLAM algorithm using an evolution strategy. This work demonstrates that an inexpensive sensor coupled with a low-speed processor are good enough to perform SLAM in simple environments in real time.

Collective and swarm robotics explores scenarios involving many robots running at the same time. A good platform for collective-robotic experiments should provide certain features among others: it should have a large battery life, it should be able to perceive its peers, and it should be capable of interacting with them. This paper presents the marXbot, a miniature mobile robot that addresses these needs. The marXbot uses differential-drive treels to provide rough-terrain mobility. The marXbot allows continuous experiments thanks to a sophisticated energy management and a hotswap battery exchange mechanism. The marXbot can self-assemble with peers using a compliant attachment mechanism. The marXbot provides high-quality vision, using two cameras directly interfaced with an ARM processor. Compared to the related work, the marXbot has better energy management, vision, and interaction capabilities. By allowing complex tasks in large environments for long durations, the marXbot opens new perspectives for the collective-robotic research.

To design, to study, and to control mixed animals-robots societies is a challenging field of scientific exploration that can bring new frameworks to study individual and collective behaviors in animal and mixed robot-animal societies. In the Chicken Robot project we aim at developing a mobile robot, able to collaborate with a group of chicks and to control certain group behaviors. The first research step is to build formal models of relevant animal behaviors by performing ethological experiments. Hence, one of the principal tasks is to design a setup equipped with appropriate monitoring tools. In this paper, we present a toolset for running chick-robot experiments and analyzing results. It includes an autonomous PoulBot robot and an experimental setup, able to autonomously record experimental video and audio data, to detect displacements of chicks and robots, to detect their calling activity and to provide robots with these data. We also present a visual data analysis system to extract behavioral features of individual chicks using the variational Bayesian Gaussian mixture model classification with a particle filters based prediction of future positions of chicks. We show how these tools are currently used to carry out chick-robot experiments, to collect behavioral data and to extract animal behavioral features that allow us to build behavioral models bound to be implemented in the robot.

Design and study of mixed animal-robot societies are the fields of scientific exploration that can bring new opportunities for research into the group behavior of social animals. Our goal is to develop a chicken robot - an autonomous mobile robot, socially acceptable by a group of chicks and able to interact with them using appropriate communication channels. One of the basic requirements to such a robot is the safety of its motion with respect to the chicks, so it has to be endowed with an efficient real-time collision avoidance system. In this paper we present a fuzzy obstacle avoidance system that was designed for the chicken robot using the theory of fuzzy relation equations. This approach allows to easily check a consistency of a used rule base and provides a more systematic approach to design of fuzzy control systems comparing with the classical techniques. The experimental results demonstrate that a mobile robot equipped with the presented system is able to successfully avoid obstacles and safely navigate on an experimental arena.

We study a simple algorithm inspired by the Brazil nut effect for achieving segregation in a swarm of mobile robots. The algorithm lets each robot mimic a particle of a certain size and broadcast this information locally. The motion of each particle is controlled by three reactive behaviors: random walk, taxis, and repulsion by other particles. The segregation task requires the swarm to self-organize into a spatial arrangement in which the robots are ranked by particle size (e.g., annular structures or stripes). Using a physics-based computer simulation, we study the segregation performance of swarms of 50 mobile robots. The robots represent particles of three different sizes. We first analyze the problem of how to combine the basic behaviors so as to minimize the percentage of errors in rank. We then show that the system is very robust to noise on inter-robot perception and communication. For a noise level of 50%, the mean percentage of errors in rank is 1%. Moreover, we investigate a simplified version of the control algorithm, which does not rely on communication. Finally, we show that the mean percentage of errors in rank decreases exponentially as the particles' size ratio increases. As the error is bounded, one can achieve 100% error-free segregation. The reduction in error, however, comes at the expense of an increase in the required sensing/communication range.

Swarm robotics draws inspiration from decentralized self-organizing biological systems in general and from the collective behavior of social insects in particular. In social insect colonies, many tasks are performed by higher order group or team entities, whose task-solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher order entities. We report on an experimental study in which a team of physical robots performs a foraging task. The robots are "identical" in hardware and control. They make little use of memory and take actions purely on the basis of local information. Our study advances the current state of the art in swarm robotics with respect to the number of real-world robots engaging in teamwork (up to 12 robots in the most challenging experiment). To the best of our knowledge, in this paper we present the first self-organized system of robots that displays a dynamical hierarchy of teamwork (with cooperation also occurring among higher order entities). Our study shows that teamwork requires neither individual recognition nor differences between individuals. This result might also contribute to the ongoing debate on the role of these characteristics in the division of labor in social insects.

The study presented in this paper explores people perception of robots, with a particular focus on domestic use. We addressed issues related to positive and negative attitudes toward robots, needs for domestic robots as well as preferences in terms of appearance and interaction modalities. We used a combined qualitative and quantitative approach using interviews and questionnaires. In total, 240 people participated in our survey. Results indicate that a large proportion of participants have a very positive attitude towards robots. They expect concrete help for a variety of tasks from these devices. They prefer a small machine and they would like to interact with robots using speech.

Mobile robots are said to be capable of self- assembly when they can autonomously form physical connections with each other. Despite the recent proliferation of self- assembling systems, little work has been done on using self- assembly to add functional value to a robotic system, and even less on quantifying the contribution of self-assembly to system performance. In this study we demonstrate and quantify the performance benefits of i) acting as a physically larger self-assembled entity, ii) using self-assembly adaptively and iii) making the robots morphologically aware (the self-assembled robots leverage their new connected morphology in a task specific way). In our experiments, two real robots must navigate to a target over a-priori unknown terrain. In some cases the terrain can only be overcome by a self-assembled connected entity. In other cases, the robots can reach the target faster by navigating individually.

This paper describes a compact robot with two magnetic wheels in a bicycle arrangement, which is intended for inspecting the inner casing of pipes with complex shaped structures. The locomotion concept is based on an adapted magnetic wheel unit integrating two lateral lever arms. These arms allow for slightly lifting off the wheel in order to locally decrease the magnetic force, as well as laterally stabilizing the wheel unit. The robot has the main advantage to be compact and mechanically simple. It features 5 active degrees of freedom: 2 driven wheels each equipped with an active lifter-stabilizer and 1 steering unit. This paper also presents the design and implementation of a prototype robot and its high mobility is shown. It is able to pass 90deg convex and concave obstacles with any inclination regarding the gravity. Finally, it only requires limited space to maneuver, since turning on spot around the rear wheel is possible.

An important goal of collective robotics is the design of control systems that allow groups of robots to accomplish common tasks by coordinating without a centralized control. In this paper, we study how a group of physically assembled robots can display coherent behavior on the basis of a simple neural controller that has access only to local sensory information. This controller is synthesized through artificial evolution in a simulated environment in order to let the robots display coordinated-motion behaviors. The evolved controller proves to be robust enough to allow a smooth transfer from simulated to real robots. Additionally, it generalizes to new experimental conditions, such as different sizes/shapes of the group and/or different connection mechanisms. In all these conditions the performance of the neural controller in real robots is comparable to the one obtained in simulation.

This paper addresses the cooperative transport of a heavy object by a group of mobile robots. We present a system in which group members lacking knowledge about the position of the transport target exploit physical interactions with other members of the group that have such knowledge. This is the first such system to achieve a performance superior to that of a passive caster. The system is fully decentralized and the information flow between the robots is limited to physical interactions. The robots have no knowledge about their relative positions. A comprehensive experimental study with up to six physical robots confirms the effectiveness, reliability, and robustness of the system. Finally, the system is examined in rough terrain conditions

We present a first attempt to accomplish a simple object manipulation task using the self-reconfigurable robotic system swarm-bot. The number of modular entities involved, their global shape or size and their internal structure are not pre-determined, but result from a self-organized process in which the modules autonomously grasp each other and/or an object. The modules are autonomous in perception, control, action, and power. We present quantitative results, obtained with six physical modules, that confirm the utility of self-assembling robots in a concrete task

This article illustrates the methods and results of two sets of experiments in which a group of mobile robots, calleds-bots, are required to physically connect to each other, that is, to self-assemble, to cope with environmental conditions that prevent them from carrying out their task individually. The first set of experiments is a pioneering study on the utility of self-assembling robots to address relatively complex scenarios, such as cooperative object transport. The results of our work suggest that the s-bots possess hardware characteristics which facilitate the design of control mechanisms for autonomous self-assembly. The control architecture we developed proved particularly successful in guiding the robots engaged in the cooperative transport task. However, the results also showed that some features of the robots' controllers had a disruptive effect on their performances. The second set of experiments is an attempt to enhance the adaptiveness of our multi-robot system. In particular, we aim to synthesise an integrated (i.e., not-modular) decision-making mechanism which allows the s-bot to autonomously decide whether or not environmental contingencies require self-assembly. The results show that it is possible to synthesize, by using evolutionary computation techniques, artificial neural networks that integrate both the mechanisms for sensory-motor coordination and for decision making required by the robots in the context of self-assembly.

In this paper, we discuss the self-assembling capabilities of the swarm-bot, a distributed robotics concept that lies at the intersection between collective and self-reconfigurable robotics. A swarm-bot is comprised of autonomous mobile robots called s-bots. S-bots can either act independently or self-assemble into a swarm-bot by using their grippers. We report on experiments in which we study the process that leads a group of s-bots to self-assemble. In particular, we present results of experiments in which we vary the number of s-bots (up to 16 physical robots), their starting configurations, and the properties of the terrain on which self-assembly takes place. In view of the very successful experimental results, swarm-bot qualifies as the current state of the art in autonomous self-assembly

This paper presents a new robotic concept, called SWARM-BOT, based on a swarm of autonomous mobile robots with self-assembling capabilities. SWARM-BOT takes advantage from collective and distributed approaches to ensure robustness to failures and to hard environment conditions in tasks such as navigation, search and transportation in rough terrain. One SWARM-BOT is composed of a number of simpler robots, called s-bots, physically interconnected. The SWARM-BOT is provided with self-assembling and self-reconfiguring capabilities whereby s-bots can connect and disconnect forming large flexible structures. This paper introduces the SWARM-BOT concept and describes its implementation from a mechatronic perspective.

In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely on the physical robot without human intervention. We show that the autonomous development of a set of behaviors for locating a battery charger and periodically returning to it can be achieved by lifting constraints in the design of the robot/environment interactions that were employed in a preliminary experiment. The emergent homing behavior is based on the autonomous development of an internal neural topographic map (which is not pre-designed) that allows the robot to choose the appropriate trajectory as function of location and remaining energy.